Tran Bach Xuan, Vu Giang Thu, Ha Giang Hai, Vuong Quan-Hoang, Ho Manh-Tung, Vuong Thu-Trang, La Viet-Phuong, Ho Manh-Toan, Nghiem Kien-Cuong P, Nguyen Huong Lan Thi, Latkin Carl A, Tam Wilson W S, Cheung Ngai-Man, Nguyen Hong-Kong T, Ho Cyrus S H, Ho Roger C M
Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi 100000, Vietnam.
Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA.
J Clin Med. 2019 Mar 14;8(3):360. doi: 10.3390/jcm8030360.
The increasing application of Artificial Intelligence (AI) in health and medicine has attracted a great deal of research interest in recent decades. This study aims to provide a global and historical picture of research concerning AI in health and medicine. A total of 27,451 papers that were published between 1977 and 2018 (84.6% were dated 2008⁻2018) were retrieved from the Web of Science platform. The descriptive analysis examined the publication volume, and authors and countries collaboration. A global network of authors' keywords and content analysis of related scientific literature highlighted major techniques, including Robotic, Machine learning, Artificial neural network, Artificial intelligence, Natural language process, and their most frequent applications in Clinical Prediction and Treatment. The number of cancer-related publications was the highest, followed by Heart Diseases and Stroke, Vision impairment, Alzheimer's, and Depression. Moreover, the shortage in the research of AI application to some high burden diseases suggests future directions in AI research. This study offers a first and comprehensive picture of the global efforts directed towards this increasingly important and prolific field of research and suggests the development of global and national protocols and regulations on the justification and adaptation of medical AI products.
近几十年来，人工智能（AI）在健康和医学领域的应用日益广泛，引发了大量研究兴趣。本研究旨在呈现关于健康和医学领域中人工智能研究的全球及历史全貌。从科学网平台检索了1977年至2018年间发表的共计27451篇论文（84.6%的论文发表于2008 - 2018年）。描述性分析考察了论文发表数量、作者及国家合作情况。作者关键词的全球网络以及相关科学文献的内容分析突出了主要技术，包括机器人技术、机器学习、人工神经网络、人工智能、自然语言处理，以及它们在临床预测和治疗中的最常见应用。与癌症相关的出版物数量最多，其次是心脏病和中风、视力障碍、阿尔茨海默病和抑郁症。此外，人工智能在一些高负担疾病应用研究方面的不足为人工智能研究指明了未来方向。本研究首次全面展示了全球在这一日益重要且成果丰硕的研究领域所做的努力，并建议制定全球和国家层面关于医疗人工智能产品合理性论证及适配的协议和法规。
